Learn Deep Learning from scratch by doing practical projects and implementing different neural networks from the ground up.
- notebooks explaining different deep learning algorithms with examples, e.g. using pytorch.
- concepts dir is where we explore different algos and concepts, at the end of each concept give the link for related projects.
- projects is where we have different projects implemented probably using different frameworks but mostly using pytorch.
- Each project should contain concepts, explanations separately from the project itself.
- After this repo, we're gonna have another one for advanced stuff, without much basics and scratchness!
- A Project for Lie Detection using facial videos
[TODO]
- Each notebook at the end should have an official, bibtex style, citations section.
- bias, variance, bias-variance tradeoff [01]
- machine learning algorithm examples [01]
[Sections]
- Transformers
- Attention
- GAN
[Other Articles (from HN)]